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Abstract:

A method for diagnosing a joint condition includes in one embodiment:
creating a 3d model of the patient specific bone; registering the
patient's bone with the bone model; tracking the motion of the patient
specific bone through a range of motion; selecting a database including
empirical mathematical descriptions of the motion of a plurality actual
bones through ranges of motion; and comparing the motion of the patient
specific bone to the database.

Claims:

1. A method of analyzing motion of a patient's bone, the method
comprising: creating a 3D model of the patient's bone; registering the
patient's bone with the bone model; tracking the motion of the patient's
bone through a range of motion; selecting a database comprising empirical
mathematical descriptions of the motion of a plurality of actual bones
through a range of motion; and comparing the motion of the patient's bone
to the database.

2. The method of claim 1 further comprising: attaching one or more
inertia based sensors near a patient's joint; gathering vibrational data
as the patient joint is moved through the range of motion; comparing the
vibrational data to an empirical database of vibration data for the
relative motion of one or more joints for diagnostic purposes, wherein
the motion of the patient's bone is tracked with the inertia based
sensors.

3. The method of claim 2 wherein each of the one or more inertia based
sensors includes an ultrasound transducer and an inertial sensor.

4. The method of claim 2 wherein attaching the one or more inertia based
sensors near the patient's joint includes releasably coupling a bone
tracking brace proximate the joint, the bone tracking brace including a
ultrasound transducer and a inertial sensor.

5. The method of claim 2 further comprising: wirelessly transmitting data
obtained by the inertial based sensor while moving the joint through the
range of motion.

6. A method of monitoring a joint, the method comprising: placing an
inertial sensor approximate the joint; gathering vibration data using the
inertial sensor while moving the joint through a range of motion; and
simultaneously with gathering the vibration data, gathering kinematics
data pertaining to the joint.

7. The method of claim 6, wherein the vibration data is time-synchronized
with the kinematics data.

8. The method of claim 6 further comprising: analyzing the vibration data
using a neural network.

9. The method of claim 8 further comprising: training the neural network.

10. The method of claim 8, wherein analyzing the vibration data using the
neural network includes diagnosing at least one of a presence and a
severity of a joint condition.

11. The method of claim 8, wherein analyzing the vibration data using the
neural network includes transmitting the vibration data across a network
and receiving data pertaining to the joint condition via the network.

12. A method of diagnosing a joint condition, the method comprising:
creating a model of a joint; obtaining vibration data and kinematics data
while moving the joint through a range of motion; correlating the
kinematics data with the model of the joint to generate a model of the
joint's motion; and analyzing the vibration data and the model of the
joint's motion to diagnose at least one of the presence and severity of a
joint condition.

13. The method of claim 12 wherein creating the model of the joint
includes creating three dimensional models of a plurality of bones of the
joint.

14. The method of claim 12 wherein creating the three dimensional models
of the plurality of bones of the joint includes conforming at least one
model bone to an actual patient bone using measured data.

17. The method of claim 16 wherein obtaining vibration data and
kinematics data includes attaching a frame proximate the joint, wherein
the frame includes at least one inertial sensor and at least one
ultrasound transducer.

18. The method of claim 12 wherein analyzing the vibration data is
performed using a neural network.

19. The method of claim 18 further comprising training the neural
network.

Description:

[0001] This Application is a continuation of U.S. patent application Ser.
No. 12/364,267, filed Feb. 2, 2009, the disclosure of which is
incorporated by reference herein in its entirety.

FIELD OF THE INVENTION

[0002] The present invention relates to diagnosis of bodily abnormalities,
and more particularly, to devices and methods for evaluating the
physiological condition of bodily tissue to discern whether abnormalities
exist and the extent of any abnormalities. While the exemplary
embodiments disclosed herein are utilized and discussed with respect to a
human knee joint, it is to be understood that other joints and bodily
tissues may be likewise diagnosed.

BACKGROUND OF THE INVENTION

[0003] In humans, the knee joint is functionally controlled by a
mechanical system governed by three unique types of forces: (1) active
forces resulting in motion, such as those resulting from muscle flexing
or relaxing; (2) constraining forces that constrain motion, such as those
resulting from ligaments being in tension; and (3) compressive forces
that resist motion, such as those acting upon bones. In addition to the
foregoing bodily tissues accounting for these three forces, cartilage and
meniscus also produce a dampening effect dissipating the compressive
forces propagating to other joints.

[0004] Knee joint motions are stabilized primarily by four ligaments,
which restrict and regulate the relative motion between the femur, tibia,
and patella. These ligaments are the anterior cruciate ligament (ACL),
the posterior cruciate ligament (PCL), the medial collateral ligament
(MCL), and the lateral collateral ligament (LCL), as shown in FIGS. 1 and
2. An injury to any of these ligaments or other soft-tissue structures
can cause detectable changes in knee kinematics and the creation of
detectable patterns of vibration representative of the type of knee joint
injury and the severity of the injury. These visual and auditory changes
are produced by the bones while moving in a distorted kinematic pattern,
and they differ significantly from the look and vibration of a properly
balanced knee joint moving through a range of motion.

[0005] Many research studies have been conducted to assess knee vibration
and correlate it with clinical data regarding various joint problems
using microphones with or without stethoscope equipment. However, it has
been shown that microphones cannot reliably detect joint frequencies,
especially those experiencing strong interference from noise, and the
signal clearance can substantially influenced by skin friction. It has
been hypothesized that the failure associated with the interpretation of
sound emissions and possible reasons for occurrence is directly
attributable to the complicity of the sound signal, the unknown noise
factors, and unknown sound center. It is desirable, therefore, to provide
a diagnostic tool that compares patient specific data with kinematic data
by providing visual feedback to clinicians.

SUMMARY OF THE INVENTION

[0006] The present invention, in one embodiment, provides a patient
specific 3D model of a patient's joint, including bone and soft tissue.
This model is then registered to the patient's actual bone so that as the
joint is taken through a range of motion it can be visualized on a
computer screen. A physician can then use the computer generated image to
make a diagnosis or compare the motion of the actual bone to a database
of clinically relevant information on desirable or undesirable joint
motion.

[0007] The exemplary embodiments of the present invention include a
diagnostic system for mammalian bodies to determine the type of injury
and extent of injury using kinematic data and/or vibration data. In
particular, an exemplary method and embodiment are directed to a knee
joint diagnostic system for automatically determining the type of injury
and the extent to which ligaments, muscles, bones, meniscus, and
cartilage may be affected by an injury through analyzing the kinematics
of the knee joint, while also analyzing the pattern and special
distribution of the vibration produced knee joint movement. An exemplary
process flow diagram for this exemplary method is shown in FIG. 3.

[0008] To evaluate knee kinematics, patient-specific 3D models of the
distal femur, proximal tibia, and the patella are constructed using pulse
echo A-mode ultrasound based 3D model reconstruction technology. In
addition, patient-specific kinematic data is obtained for the motions of
the femur, tibia, and patella using pulse A-mode ultrasound. Finally,
patient specific vibration data is obtained while the knee joint is taken
through a range of motion and loaded in real-world conditions. In
exemplary form, the vibration data and kinematic data are taken at the
same time using the single data acquisition device. In a further
exemplary embodiment, if the data is acquired in a physician's office,
the data is displayed in real-time on a split screen monitor. If,
however, the data is acquired outside of the doctor's office, a recording
device and memory may be utilized to record the data in a time synched
manner. In a yet a further exemplary embodiment, the patient may be given
an actuator that is operative to note the general time frame within which
the patient felt a particular pain or severe pain to allow a correlation
between pain felt by the patient and the kinematics and vibration
occurring at roughly the same time.

[0009] Patient-specific data is analyzed by a trained neural network in
order to provide an automated output as to the existence of an injury,
the type of injury, and the severity of the injury. This neural network
may be accessible via the internet or may reside on a physician's local
computer. In addition, or in the alternative, patient-specific data may
be analyzed by a physician to make the diagnosis directly without the aid
of the neural network.

[0010] Using the exemplary methods and devices as disclosed herein, a
physician may diagnose a bodily injury without requiring experimental
surgery or requiring exposure of the patient to radiation from still
X-rays or fluoroscopy. In addition, the data taken regarding each patient
is continuous through a range of motion, in contrast to X-rays and
fluoroscopy which take images at distinct points with significant range
of motion gaps. In addition, data taken in accordance with the exemplary
method and devices disclosed herein also contrasts data taken by a
magnetic resonance imaging machine, not only because the data taken is
continuous along the range of motion, but also because the bodily portion
evaluated is acting under loaded conditions in a dynamic environment.

[0011] It is an object of the present invention to provide a method of
creating a three dimensional model of a patient's bone using tracked
pulse-echo A-Mode ultrasound and atlas-based deformable models.

[0012] It is another object of the present invention to provide a method
of registering a patient's bone with a three dimensional model of the
patient's actual bone.

[0013] Another object of the present invention is to provide a method of
tracking the motion of a patient's actual bone through space and showing
the same on a computer screen.

[0014] Yet another object of the present invention is to provide a method
of tracking at least two bones relative to one another as three
dimensional models on a computer screen as the actual bones are taken
through a range of motion.

[0015] It is also an object of the present invention to provide a method
of diagnosis for joint conditions based on a database of kinematic or
other information about joint motion.

BRIEF DESCRIPTION OF THE DRAWINGS

[0016]FIG. 1 is a posterior view of a human knee joint in a fixed
position;

[0017]FIG. 2 is a posterior view of a human knee joint in an extended
position;

[0018]FIG. 3 is an exemplary process flow diagram using exemplary methods
within the scope of the present invention;

[0019]FIG. 4 is a schematic diagram of the modules of an exemplary
diagnostic system;

[0020]FIG. 5 is a screen shot of a software user interface for bone
modeling;

[0021] FIG. 6 is an anterior view of the bones of a human knee joint in an
extended position;

[0022]FIG. 7 is a pictorial representation of a human leg having an
exemplary brace attached to a distal segment of the femur, and exemplary
brace attached to a proximal segment of the tibia, a sensor mounted
proximate the patella, and a foot pressure sensing shoe;

[0023]FIG. 8 is a is an illustration of a CT slice of the transcutaneous
detection of a bone's surface using pulse echo A-mode ultrasound;

[0024]FIG. 9 is a schematic of an exemplary inertia-based localizer
circuit;

[0025]FIG. 10 is a schematic of an exemplary brace circuit architecture;

[0026]FIG. 11 is a circuit schematic of an exemplary high voltage
amplifier;

[0027]FIG. 12 is a circuit layout for the exemplary high voltage
amplifier of FIG. 11;

[0028]FIG. 13 is a block diagram for an exemplary high voltage
multiplexer;

[0029] FIG. 14 is a block diagram for an exemplary receiving circuit;

[0030] FIG. 15 is a pictorial representation of an exemplary kinematics
tracking brace;

[0031]FIG. 16 is a pictorial representation of an alternative exemplary
kinematics tracking brace;

[0032]FIG. 17 is a pictorial representation of a further alternative
kinematics exemplary tracking brace;

[0033]FIG. 18 is a pictorial representation of a vibration detection
module;

[0036] FIGS. 21A, 21B, and 21C are a series of views showing contact path
tracking in accordance with the exemplary embodiments;

[0037]FIG. 22 is a is a schematic of the overall classification system
flow chart;

[0038]FIG. 23 is a schematic representation of an exemplary neural
network classifier;

[0039]FIG. 24 is an exemplary process flow for training an exemplary
neural network; and

[0040]FIG. 25 is an exemplary process flow for knee deficiency diagnosis
using a trained neural network.

DETAILED DESCRIPTION

[0041] The exemplary embodiments of the present invention are described
and illustrated below to encompass diagnosis of bodily abnormalities and,
more particularly, devices and methods for evaluating the physiological
condition of bodily tissue to discern whether abnormalities exist and the
next of any abnormalities. Of course, it will be apparent to those of
ordinary skill in the art that the preferred embodiments discussed below
are exemplary in nature and may be reconfigured without departing from
the scope and spirit of the present invention. However, for clarity and
precision, the exemplary embodiments as discussed below may include
optional steps, methods and features that one of ordinary skill should
recognize as not being a requisite to fall within the scope of the
present invention. In exemplary fashion, the embodiments disclosed herein
are described with respect to diagnosing a knee joint injury.
Nevertheless, the embodiments may be utilized to diagnose other joints
and bodily tissue injuries, as the knee joint is merely exemplary to
facilitate an understanding of the embodiments disclosed.

[0042] Referencing FIG. 4, a first exemplary diagnostic system includes
four modules: (1) a pulse echo A-mode ultrasound based 3D model
reconstruction (PEAUMR) module for constructing 3D patient specific
models of the knee joint bones; (2) a joint kinematics tracking (JKT)
module for tracking kinematics of the knee joint using the
patient-specific 3D model of the knee joint from the PEAUMR module; (3) a
vibration detection (VD) module for capturing sounds emanating from the
knee joint while in motion; and (4) an intelligent diagnosis (ID) module
for identifying pathological cases of the knee joint using kinematic data
and associated vibration data gathered during the joint motion. Each of
these four modules is described in further detail in the following
sections. The foot sensor interacts in real time with these other modules
providing dynamic force data.

[0043] It will be understood by those of skill in the art that the system
described above is usable with or without the use of the vibration
detection module. For example, one may use the present invention by
mathematically describing the relative motion of bones in a patient's
joint as such motion is tracked in a 3D patient specific bone model and
comparing such description with a database of mathematical descriptions
of joint motion. The database could contain mathematical descriptions of
healthy or clinically undesirable joint motion.

[0044] Referring to FIG. 5, the PEAUMR module constructs a 3D model of a
subject's (e.g., a patient) bones by transcutaneously acquiring a set of
3D data points that in total are representative of the shape of the
bone's surface using a tracked pulse echo A-mode ultrasound probe. The
probe consists of a single ultrasound transducer attached to a global
localizer. The global localizer may be optical, inertial, electromagnetic
or ultra wide band radio frequency. The probe is battery-powered and
connected wirelessly to a computer in order to record the set points and
construct a unique or patient-specific bone model using an atlas-based
deformable model technique.

[0045] The computer includes software that interprets data from the
tracked pulse echo A-mode ultrasound probe and is operative to construct
the 3D models of the patient's bones, which will look very similar to the
model shown in FIG. 6. The patient-specific bone is reconstructed using
the set of points collected from the bone's surface transcuateously by
the tracked ultrasound probe. These points are then used by the
atlas-based deformable model software to reconstruct the 3D model of the
patient's bone.

[0046] In exemplary form, the software includes a plurality of bone models
of the femur, tibia, and patella that are classified, for example, based
upon ethnicity, gender, skeletal bone to be modeled, and the side of the
body the bone is located. Each of these classifications is accounted for
by the dropdown menus of the software so that the model initially chosen
by the software most closely approximates the bone of the patient.

[0047] After the software selects the bone model to approximate the bone
of the patient, the ultrasound transducer probe is repositioned on the
exterior of the skin and data points are generated and applied to the
model bone (in this case a distal femur), numerically recorded and
viewable in a data window, and ultimately utilized by the software to
conform the bone model to the patient's actual bone shape. Obviously, a
higher number of data points imposed on the model will generally result
in a more accurate patient model. Nevertheless, in view of the model
bones already taking into account numerous traits of the patient
(ethnicity, gender, bone modeled, and body side of the bone), it is quite
possible to construct an accurate patient-specific 3D model with as few
as 150 data points, which typically can be taken by repositioning the
probe over the bone for 30 seconds for each bone. In this example, it is
preferable for the data to be acquired both while the knee is bent and
extended to more accurately shape the ends of the bones. This same
procedure is repeated for the remaining bones of the joint, in this case
the proximal end of the tibia and the patella, in order for the software
to combine the bones thereby forming the joint. Ultrasound will not be
affected whether the patient has normal or prosthetic implant. The 3D
model of the femur can be resected and attached with the implanted CAD
model.

[0048] Referring to FIG. 7, the JKT module tracks the kinematics of the
knee joint using the patient-specific 3D bone models from the PEAUMR
module. In this exemplary embodiment, motion tracking of the patient's
knee joint bones is performed using one or more bone motion tracking
braces. In exemplary form, the bone motion tracking brace includes pulse
echo A-mode ultrasound transducers to transcutaneously localize points on
the bones surface. Incidentally, the pulse echo A-mode ultrasound
transducers may or may not be identical to the pulse echo A-mode
ultrasound transducers used by the PEAUMR module. Commercially available
transducers for use with the exemplary embodiments include, without
limitation, the Olympus immersion unfocused 3.5 MHz transducer. The force
sensing shoe detects the ground reactive pressures simultaneous with knee
joint kinematic data acquisition.

[0049] Each ultrasound transducer is tracked using an accelerometer or a
sensor-specific localizer (or any other appropriate inertial sensor). The
resulting localized bone points generated from the outputs of the
ultrasound transducers are used in combination with the patient specific
3D bone models to discern bone movement while the knee joint is taken
through a range of motion. In exemplary form, three braces and a foot
force sensing shoe are used to track knee joint kinematics and dynamic
forces: (a) a first brace is positioned proximate the distal portion of
the femur; (b) a second brace is positioned proximate the distal end of
the tibia; and, (c) a third brace is positioned proximate the patella
region.

[0050] Referring to FIG. 8, an exemplary bone motion tacking brace
includes a plurality of pulse echo A-mode ultrasound transducers for
transcutaneous detection of the bone's surface and inertia-based
localizers to track the motion of the ultrasound transducers, which in
turn, track the bones motion. Each brace is wirelessly connected to a
computer operative to perform computations and visualization in real-time
showing movements of the patient-specific 3D bone models paralleling
movements of the patient's actual knee joint in a time synchronized
manner. Each exemplary brace include a rigid or semi-rigid body having a
plurality (two or more) of complementary metal oxide semiconductor (CMOS)
inertia-based sensors attached thereto. The position of each sensor
and/or transducer is tracked by using the equation of motion:
Fr+Fr*=0, where, Fr is a summation of all the generalized
active forces in the system, and Fr* is a summation of all the
generalized inertia forces in the system. The homogenous transformation
between the localizer's reference coordinate frame and the world
coordinate frame is calculated using the positions of multiple inertia
sensors. The following equation calculates the linear movement of the
transducer: v(n+1)=v(n)+a(n)dt and s(n+1)=s(n)+v(n)dt-0.5a(n)dt2, where
s(n+1) is position at the current state, s(n) is the position from the
previous state, v(n+1) is instantaneous velocity of the current state,
v(n) is the velocity from previous state, and a(n) is the acceleration
from the accelerometer and dt is the sampling time interval. The previous
equations describe the dynamic motion and positioning of a point in 3D
Euclidean space. Additionally information is needed to describe a 3D body
orientation and motion. The orientation of the transducer can be
described by using a gravity based accelerometer (example: ADXL-330,
analog device) by extracting the tilting information from each pair of
orthogonal axis. The acceleration output on x, y, or z due to gravity is
equal to the following: Ai=(Vouti-Voff)/S, where Ai is the
acceleration at x, y, or z axis, Vouti is the voltage output from
the x, y, or z axis, Voff is the offset voltage, and S is the sensitivity
of the accelerometer. The yaw, pitch and row can be calculated as shown
in the following:

where pitch is ρ, which is x-axis relative to the ground, roll is
φ, which is y-axis relative to the ground, and row is θ, which
is z-axis relative to the ground. Since the accelerometer is based using
gravity, the orientation does not require information from the previous
state once the sensor is calibrated. The static calibration requires the
resultant sum of accelerations from the 3 axis equal to 1 g.
Alternatively, an orientation sensor that gives us yaw, pitch, and row
information of the body are also commercially available (example:
IDG-300, Invensense). The orientation of the transducer can then be
resolved by using direction cosine matrix transformation:

[0051] Referring to FIG. 9, an accelerometer based localizer is used to
track each pulse echo A-mode ultrasound transducer mounted to the brace.
The localizer comprises a plurality of nodes, with each node comprising a
CMOS accelerometer and a temperature sensor for thermal drift comparison.
Each node is integrated to minimize noise and distortion. The outputs of
the accelerometers regarding the X, Y, and Z coordinates and temperature
sensor are directed to a multiplexer that multiplexes the signals.
Multiplexed outputs are amplified by an amplifier and then directed to an
analog-to-digital converter. The digital conversion of the signal can be
performed within or outside the CMOS sensors chip. Outputted digital
signals are directed to a wireless transmitter by way of a parallel
input/serial output device.

[0052] Referring to FIG. 10, each of the three exemplary design
alternatives for the brace has a similar electronic architecture. An
exemplary electronic architecture includes a high voltage amplifier
circuit feeding a voltage multiplexer circuit to excite each ultrasound
transducer and thereby acting as an analog switch. The echo signals from
each transducer are multiplexed pursuant to a logic control directing the
opening of the switches in the multiplexer circuit at precise intervals.
An exemplary logic control is the MSP430 available from Texas
Instruments. The output from the multiplexer circuit is amplified by an
amplifier circuit, signal conditioned using a signal conditioning
circuit, and digitized using an analog-to-digital converter. Electric
power to the foregoing components is supplied by way of a battery, which
also supplies power to a wireless transmitter module. In exemplary form,
the wireless transmitter module utilizes the universal asynchronous
receiver/transmitter (UART) protocol. The module includes a wireless
transmitter circuit receiving the output of the first in-first out (FIFO)
buffer of the analog-to-digital converter by way of a serial interface.
An output from the wireless transmitter circuit is conveyed using a
serial link coupled to an antenna. Signals conveyed through the antenna
are broadcast for reception by a wireless receiver coupled to a
controller computer.

[0053] Referring to FIGS. 11 and 12, an exemplary high voltage circuit is
utilized to trigger and generate the excitation energy for the
piezoelectric crystal in the transducer. Exemplary high voltage circuits
for use in this embodiment include, without limitation, the pulsar
integrated circuit (HV379) available from Supertex.

[0054] Referencing FIG. 13, an exemplary high voltage multiplexer is
utilized to trigger and excite multiple piezoelectric transducers without
increasing the number of high voltage circuits mentioned with regard to
FIG. 11. Exemplary high voltage multiplexers for use in this embodiment
include, without limitation, the high voltage multiplexer (HV2221)
available from Supertex. The advantage of using a high voltage
multiplexer is the ability to use CMOS level control circuitry, thereby
making the control logic compatible with virtually any microcontroller or
field programmable gate array commercially available.

[0055] Referring to FIG. 14, an exemplary receiving circuit, which
comprises the multiplexer circuit, the amplifier circuit, the signal
conditioning circuit, and the analog-to-digital converter, is utilized to
receive the echo signals from each transducer. Exemplary receiving
circuits for use in this embodiment include, without limitation, the
AD9271 8-channel ultrasound receiving integrated circuit available from
Analog Devices.

[0056] Referring to FIG. 15, a first exemplary bone tracking brace
includes a plurality of transducers mounted thereto. Each transducer is
responsible for determining the location of a point on the surface of the
bone for each motion tracking frame. Problems of locating and tracking
the bone using ultrasound data are reduced as the motion of the bone
relative to the skin is small compared to the gross joint motion. There
are at least three approaches disclosed herein for tracking the motion of
the ultrasound transducers themselves. The first approach, commonly
referred to herein as the ITT (Individual transducer tracking) approach,
involves each transducer in the brace having an inertia-based localizer
to individually track each transducer. Using the ITT approach, in
exemplary form, the transducers are held together by flexible length
straps.

[0057] Referencing FIG. 16, a second approach, commonly referred to herein
as the ITML (Inter-transducers Mechanical Links) approach, involves the
transducers being connected to each other by movable mechanical links.
Each mechanical link includes length and angle sensors that allow for
detection of the movement of the transducers relative to one another and
the relative translational motions of the links. Every two links are
connected by a pivot pin that allows rotation and translation of the
links relative to each other. An angle sensor is mounted to at least one
link proximate the pivot pin to allow for detection of the angle between
the links. The ITML approach features less localizers than the individual
transducer tracking design.

[0058] Referring to FIG. 17, a third approach, commonly referred to herein
as the RT (Rotating Transducer) approach, involves using a single
ultrasound transducer that is mounted to a carriage. The carriage
traverses along a track located on the inner circumference of the brace.
For example, the carriage may be moved along the tack by a string loop
that is wrapped around the drive shaft of a motor. When the transducer
reaches the motor, the rotation direction of the motor is changed and the
transducer moves in the opposite direction.

[0059] An inertia-based localizer is mounted to the transducer to track
its motion. As the transducer rotates within the inner circumference of
the brace, it collects data as to the outer circumferential topography of
the bone surface. By using a single transducer, the RT approach includes
the advantage of lower cost than the stationary transducer designs and
higher accuracy due to the greater number of localized bone surface
points for each tracking step, while maintaining a mechanical
flexibility.

[0060] Referring to FIG. 18, a third module of the exemplary diagnostic
system, the vibration detection module, includes thin film accelerometers
that detect the vibration produced by motion of the knee joint. Thin film
accelerometers are used in lieu of sound sensors, because of better
performance and less noise susceptibility. In exemplary form, the thin
film accelerometers may be the same ones used for the localizer, as well
as having the same circuitry for driving the accelerometers. The
accelerometers are attached to the patients and communicatively connected
to the kinematic tracking braces so the outputs from the accelerometers
can be amplified, digitized, and sent wirelessly to the controller
computer.

[0061] Referring to FIG. 22, X-ray video fluoroscopy and in-vivo
measurements of dynamic knee kinematics are important for understanding
the effects of joint injuries, diseases, and evaluating the outcome of
surgical procedures. In exemplary form, using the two aforementioned
techniques, six degrees of freedom (DOF) are determined between the femur
and tibia, femur and patella, and tibia and patella that involve the
position and orientation of each with respect to the other. The accuracy
of this approach is within one degree of rotation and one mm of
translation (except for translation parallel to the viewing direction).
Although this approach is highly accurate, it constrains the patient to
remain within the small working volume of the fluoroscope unit and
subjects the patient to ionizing radiation for a prolonged period of
time. For most dynamic activities where the joints are loaded such as
running, jumping, or other dynamic activities, fluoroscopy is an
unacceptable alternative. To address this deficiency in preexisting
approaches, an exemplary system accurately measures joint motion during
dynamic activities using a portable brace, such as those previously
discussed herein. By using a portable brace having sensors mounted
thereto, X-ray fluoroscopy may be omitted.

[0062] Implementation of joint movement visualization includes using the
exemplary 3C model reconstruction using pulse-echo A-mode ultrasound
system to measure vibrations produced to accurately localize the exact
vibration center and causes for its occurrence. The interpretation of the
vibration and kinematic data is a complicated task involving an in-depth
understanding of data acquisition, training data sets and signal
analysis, as well as the mechanical system characteristics. Vibrations
generated through the implant components, bones, and/or soft tissues
interaction result from a forced vibration induced by driving force
leading to a dynamic response. The driving force can be associated with
the impact following knee ligament instability, bone properties, and
conditions. A normal, intact knee will have a distinct pattern of motion,
coupled with distinct vibrational characteristics. Once degeneration or
damage occurs to the knee joint, both the kinematic patterns and
vibrational characteristics become altered. This altering, for each type
of injury or degeneration, leads to distinct changes that can be captured
using both kinematic and vibration determination.

[0063] Referencing FIG. 25, a fourth module of the exemplary diagnostic
system, the intelligent diagnosis module, which is a software module, is
operative to diagnose ligament, other soft tissue, and bone injuries.
From previous studies, normal and anterior cruciate ligament deficient
(ACLD) knee subjects exhibit a similar pattern of posterior femoral
translation during progressive knee flexion, but the subjects exhibit
different axial rotation patterns of 30 degrees of knee flexion.
Accordingly, the diagnostic module is a two stage device that includes a
first stage involving motion measurement extraction, while a second stage
classifies any injury that is detected.

[0064] This first stage includes acquisition of kinematic feature vectors
using multiple physiological measurements taken from the patient while
the patient moves the joint in question through a range of motion.
Exemplary measurements include, without limitation, medical condyle
anteroposterior motion (MAP) and lateral condyle anteroposterior (LAP),
with the latter pertaining to the anterior-posterior NP distance of the
medial and lateral condyle points relative to the tibia geometric center.
Other exemplary measurements include LSI (distance between the lateral
femoral condyle and the lateral tibial plateau) and MSI (distance between
the medial femoral condyle and the medial tibial plateau) which are S/I
(superior/inferior) distances of the lateral and medial condyle points to
the tibial plane. Further exemplary measurements include condyle
separation, which is the horizontal (x-y plane) distance between the two
minimum condyle points to the tibia (See FIG. 21). Feature vectors also
include the femoral position with respect to the tibia which is defined
by three Euler angles and three translation components in addition to the
vibration signal, and force data (see FIGS. 19A, 19B, and 19C). FIG. 20
is an exemplary graphical representation showing average ACLD medial and
lateral condyle contact positions during a deep knee bend activity.

[0065] Referring to FIG. 22, the motion features vectors extracted from
the kinematic and vibration analyses are output to a multilayer back
propagation neural network for determining the injured ligament.

[0066] Referencing FIG. 23, an exemplary neural network classifier has
multiple binary outputs. Each output is either a one or zero, with one
corresponding to yes and zero corresponding to no. In this exemplary
neural network classifier, each output represents the response of the
neural network to a particular injury type; for example one output will
represent the response for anterior cruciate ligament deficiency (ACLD),
its state will be one if an ACL injury is detected, and zero otherwise.
Obviously, the neural network may be significantly more sophisticated or
less sophisticated, depending upon the underlying model of the joint in
question.

[0067] Referring to FIG. 24, construction of the exemplary neural network
(NN) includes formulating a supervised classifier using a training set of
the kinematic and vibration data corresponding to normal and injured knee
joints. The NN is trained with a set of vectors. Each vector consists of
data (kinematics, vibrations and forces) collected from one joint.
Fluoroscopy data can be used to calculate the kinematics. Once the NN is
trained, it can be used to classify new cases and categorize the injury
type using these kinematics, vibration and forces data. Those skilled in
the art will readily understand that the types and classifications
desired to be accommodated by the neural network necessarily include
training the neural network on these very types and classifications.
Exemplary types and classifications of injuries to a mammalian knee joint
include, without limitation, osteoarthritic conditions, soft tissue
damage, and abnormal growths. Likewise, the neural network also needs to
be trained as to indicators of normal knee function. In this manner, once
the neural network is trained, it has the capability to differentiate
between and output diagnosis data concerning normal and abnormal knee
conditions.

[0068] Referencing FIG. 25, the vibration and kinematics features of a
person's knee joint are compiled and fed to the trained neural network.
The trained neural network then diagnoses the condition of the patient's
knee joint, identifying and degeneration by type and severity.

[0069] Exemplary embodiments may be adapted to collect data outside of a
clinical setting. For example, an exemplary embodiment may be worn by a
patient for an extended period of time while performing normal
activities. For example, a patient may wear vibration sensors and/or a
kinematics tracking brace during activities that are not reproducible in
the office (for example, weight lifting, racquet ball etc.) that elicit
the pain or symptom. In some embodiments, the patient may turn the device
on immediately prior to the activity and/or the patient may mark the
event when it occurs. This enables analysis of the data just a few
seconds before the marked time to see what abnormal sounds or joint
kinematic were occurring. Data may be stored on a portable hard drive (or
any other portable storage device) and then may be downloaded to
exemplary systems for analysis. The data can be transmitted and stored in
a computer wirelessly. It can also be stored with a miniature memory
drive if field data is desired. If the occurrence of the pain was more
random, exemplary devices allow continuous gathering of data. In
embodiments, the patient may mark the event. Devices capable of
continuous monitoring may require a larger data storage capacity.

[0070] It is also understood that while the exemplary embodiments have
been described herein with respect to a knee joint, those skilled in the
art will readily understand that the aforementioned embodiments may be
easily adapted to other joints of a mammalian animal. For example,
embodiments may be adapted for use on hips, ankles, toes, spines,
shoulders, elbows, wrists, fingers, and temporomandibular joints.

[0071] Following from the above description and invention summaries, it
should be apparent to those of ordinary skill in the art that, while the
methods and apparatuses herein described constitute exemplary embodiments
of the present invention, the invention contained herein is not limited
to this precise embodiment and that changes may be made to such
embodiments without departing from the scope of the invention as defined
by the claims. Additionally, it is to be understood that the invention is
defined by the claims and it is not intended that any limitations or
elements describing the exemplary embodiments set forth herein are to be
incorporated into the interpretation of any claim element unless such
claim limitation is explicitly stated. Likewise, it is to be understood
that it is not necessary to meet any or all of the identified advantages
or objects of the invention disclosed herein in order to fall within the
scope of any claims. Since the invention is defined by the claims and
since inherent and/or unforeseen advantages of the present invention may
exist even though they any not have been explicitly discussed herein.